Hamajima N, Sasaki R, Shibata A, Suzuki S, Tamakoshi A, Aoki K
Nihon Gan Chiryo Gakkai Shi. 1989 May 20;24(5):1015-9.
Estimates of hazard ratio from hypothetical survival data were examined, under the condition that the survival is affected by two prognostic factors X1 and X2 with two discrete values (0, 1) which are strongly correlated. Survival curves from the data were assumed to fit an exponential function. As an example, following numbers of subjects were used; 100 cases with X1 = 0 and X2 = 0, 10 cases with X1 = 1 and X2 = 0, 10 cases with X1 = 0 and X2 = 1, and 100 cases with X1 = 1 and X2 = 1. When the hazard ratio of X1 = 1 or X2 = 1 to X1 = 0 and X2 = 0 was 3, univariate analysis gave reasonable estimates around 3. Under the same condition, estimates of hazard ratio from multivariate analysis including the two variables X1 and X2 varied widely, depending on the survivals of 20 cases with X1 = 1 X2 = 0 or X1 = 0 X2 = 1. Probability of having extremely deviated estimates was demonstrated. The result illustrated successfully some points we should take into account when proportional hazard models including strongly correlated variables are applied.
在生存受到两个预后因素X1和X2影响的情况下，对来自假设生存数据的风险比估计值进行了检验，这两个因素具有两个离散值（0,1）且高度相关。假设数据的生存曲线符合指数函数。例如，使用了以下数量的受试者：100例X1 = 0且X2 = 0，10例X1 = 1且X2 = 0，10例X1 = 0且X2 = 1，以及100例X1 = 1且X2 = 1。当X1 = 1或X2 = 1相对于X1 = 0且X2 = 0的风险比为3时，单变量分析给出了约为3的合理估计值。在相同条件下，包含变量X1和X2的多变量分析得出的风险比估计值差异很大，这取决于20例X1 = 1且X2 = 0或X1 = 0且X2 = 1患者的生存情况。结果证明了出现极端偏差估计值的可能性。该结果成功说明了在应用包含高度相关变量的比例风险模型时应考虑的一些要点。
